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RISS 인기검색어
- 검색키워드
- 저자 : Jagannathan Sarangapani (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Decentralized Neural Network-based Excitation Control of Large-scale Power Systems
Wenxin Liu,Jagannathan Sarangapani,Ganesh K. Venayagamoorthy,Li Liu,Donald C. Wunsch II,Mariesa L. Crow,David A. Cartes 대한전기학회 2007 International Journal of Control, Automation, and Vol.5 No.5
This paper presents a neural network based decentralized excitation controller design for large-scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem control activities are guaranteed through rigorous stability analysis. Neural networks in the controller design are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded. To evaluate its performance, the proposed controller design is compared with conventional controllers optimized using particle swarm optimization. Simulations with a three-machine power system under different disturbances demonstrate the effectiveness of the proposed controller design.
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Decentralized Fault Tolerant Control of a Class of Nonlinear Interconnected Systems
Hasan Ferdowsi,Sarangapani Jagannathan 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.2
In this paper, a novel decentralized fault tolerant controller (DFTC) is proposed for interconnected nonlinearcontinuous-time systems by using local subsystem state vector alone in contrast with traditional distributedfault tolerant controllers or fault accommodation schemes where the measured or the estimated state vector of theoverall system is needed. The proposed decentralized controller uses local state and input vectors and minimizes thefault effects on all the subsystems. The DFTC in each subsystem includes a traditional controller term and a neuralnetwork based online approximator term which is used to deal with the unknown parts of the system dynamics,such as fault and interconnection terms. The stability of the overall system with the proposed DFTC is investigatedby using Lyapunov approach and the boundedness of all signals is guaranteed in the presence of a fault. Therefore,the proposed controller enables the system to continue its normal operation after the occurrence of a fault, as longas it does not cause failure or break down of a component. Although the decentralized fault tolerant controller isdesigned mainly for large-scale systems where continuous transmissions between subsystems is not possible, it canalso be applied to small-scale systems where sensor measurements are available for use in all subsystems. Finallythe proposed methods are verified and compared in simulation environment.
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Hasan Ferdowsi,Jia Cai,Sarangapani Jagannathan 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.3
This paper presents a new model-based fault detection and failure prediction framework for a class of multi-input and multi-output (MIMO) nonlinear distributed parameter systems (DPS) described by partial differential equations (PDE) with actuator and sensor faults. The fault functions cover both abrupt and incipient faults. A Luenberger type observer is used to monitor the health of the DPS as a detection observer on the basis of the nonlinear PDE representation of the system and by utilizing only the measured output vector. By taking the difference between measured and estimated outputs, a residual signal is generated for fault detection. If the detection residual exceeds a predefined threshold, a fault is claimed to be active. Once an actuator or a sensor fault is detected, an appropriate fault parameter update law is developed to learn the fault dynamics online with the help of an additional measurement. Later, an explicit formula is introduced to estimate the time-to-failure in the presence of an actuator/sensor fault by utilizing the limiting values of the output vector along with the estimated fault parameter vector. Eventually, the effectiveness of the proposed detection and prediction framework is demonstrated on a nonlinear process.
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